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Cattle weight estimation using active contour models and regression trees Bagging

2020; Elsevier BV; Volume: 179; Linguagem: Inglês

10.1016/j.compag.2020.105804

1872-7107

Vanessa Weber, Fabricio de Lima Weber, Adair da Silva Oliveira, Gilberto Astolfi, Geazy Vilharva Menezes, João Vitor de Andrade Porto, Fábio Prestes Cesar Rezende, Pedro Henrique de Moraes, Edson Takashi Matsubara, Rodrigo Gonçalves Mateus, Thiago Luís Alves Campos de Araújo, Luiz Otávio Campos da Silva, Eduardo Quirino Arguelho de Queiroz, U. G. P. de Abreu, Rodrigo da Costa Gomes, Hemerson Pistori,

Animal Behavior and Welfare Studies

Monitoring the weight of beef cattle is important for productive strategies. The main goal of this work was to automatically extract measurements from 2D images of the dorsal area of Nellore cattle to estimate the weight of these cattle using regression algorithms. For this purpose, Euclidean distances from points generated by the Active Contour Model, together with features obtained from the dorsal Convex Hull, were selected. These were submitted to Bagging, Regression by Discretization and Random Forest algorithms for analysis of the predicted error metrics. The Bagging algorithm showed the best results, with Mean Absolute Error (MAE) of 13.44 kg (±2.76), Square Root of the Mean Error (RMSE) of 15.88 kg (±2.86), Mean Absolute Percentage Error (MAPE) of 2.27% and correlation coefficient at 0.75.